Ascertaining Uncertainty for Efficient Exact Cache Analysis

Touzeau, Valentin; Maïza, Claire; Monniaux, David; Reineke, Jan

doi:10.1007/978-3-319-63390-9_2

Cited by 19 publications

(34 citation statements)

References 18 publications

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“…Ferdinand et al [18] use abstract interpretation to model set associative LRU caches. Model-checking [13,35] increases the accuracy of this analysis that distinguishes always hit, always miss, and not classified. Touzeau et al [36] show how to attain high accuracy without costly model-checking.…”

Section: Related Workmentioning

confidence: 99%

A fast analytical model of fully associative caches

Gysi

Grosser

Brandner

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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While the cost of computation is an easy to understand local property, the cost of data movement on cached architectures depends on global state, does not compose, and is hard to predict. As a result, programmers often fail to consider the cost of data movement. Existing cache models and simulators provide the missing information but are computationally expensive. We present a lightweight cache model for fully associative caches with least recently used (LRU) replacement policy that gives fast and accurate results. We count the cache misses without explicit enumeration of all memory accesses by using symbolic counting techniques twice: 1) to derive the stack distance for each memory access and 2) to count the memory accesses with stack distance larger than the cache size. While this technique seems infeasible in theory, due to non-linearities after the first round of counting, we show that the counting problems are sufficiently linear in practice. Our cache model often computes the results within seconds and contrary to simulation the execution time is mostly problem size independent. Our evaluation measures modeling errors below 0.6% on real hardware. By providing accurate data placement information we enable memory hierarchy aware software development.CCS Concepts • Software and its engineering → Software performance; Compilers.

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Section: Related Workmentioning

confidence: 99%

A fast analytical model of fully associative caches

Gysi

Grosser

Brandner

et al. 2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

View full text Add to dashboard Cite

show abstract

“…In this paper, we develop an analysis based on abstract interpretation that comes close to the efficiency of the classical approach by Ferdinand and Wilhelm [14] while achieving exact classification of all memory accesses as the modelchecking approach by Touzeau et al [38]. In other terms, we introduce an exact and scalable analysis by carefully refining the abstraction and using suitable algorithms and data structures.…”

Section: Contributionsmentioning

confidence: 99%

“…Recently, Touzeau et al [38] proposed an exact analysis, i.e., it exactly classifies memory accesses with respect to the concrete semantics, into "always hit", "always miss", or "hits or misses depending on the execution". Their approach encodes the concrete cache state transitions into a reachability problem, fed to a symbolic model checker.…”

Section: Cache Organization Cache Analysis and The Stateof-the-artmentioning

confidence: 99%

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Fast and exact analysis for LRU caches

Touzeau

Maïza

Monniaux

et al. 2019

Proc. ACM Program. Lang.

Self Cite

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For applications in worst-case execution time analysis and in security, it is desirable to statically classify memory accesses into those that result in cache hits, and those that result in cache misses. Among cache replacement policies, the least recently used (LRU) policy has been studied the most and is considered to be the most predictable.The state-of-the-art in LRU cache analysis presents a tradeoff between precision and analysis efficiency: The classical approach to analyzing programs running on LRU caches, an abstract interpretation based on a range abstraction, is very fast but can be imprecise. An exact analysis was recently presented, but, as a last resort, it calls a model checker, which is expensive.In this paper, we develop an analysis based on abstract interpretation that comes close to the efficiency of the classical approach, while achieving exact classification of all memory accesses as the model-checking approach. Compared with the model-checking approach we observe speedups of several orders of magnitude. As a secondary contribution we show that LRU cache analysis problems are in general NP-complete.

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“…There are also techniques for improving the accuracy of cache analysis, e.g., by using symbolic execution or model checking to refine the cache analysis results [13,40,56] and by extending the analysis from single-core to multi-core CPUs [38]. However, none of these techniques considered speculative execution, which is the focus of our work.…”

Section: Related Workmentioning

confidence: 99%

Abstract interpretation under speculative execution

Wang

2019

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Analyzing the behavior of a program running on a processor that supports speculative execution is crucial for applications such as execution time estimation and side channel detection. Unfortunately, existing static analysis techniques based on abstract interpretation do not model speculative execution since they focus on functional properties of a program while speculative execution does not change the functionality. To fill the gap, we propose a method to make abstract interpretation sound under speculative execution. There are two contributions. First, we introduce the notion of virtual control flow to augment instructions that may be speculatively executed and thus affect subsequent instructions. Second, to make the analysis efficient, we propose optimizations to handle merges and loops and to safely bound the speculative execution depth. We have implemented and evaluated the proposed method in a static cache analysis for execution time estimation and side channel detection. Our experiments show that the new method, while guaranteed to be sound under speculative execution, outperforms state-of-the-art abstract interpretation techniques that may be unsound.CCS Concepts • Software and its engineering → Formal software verification; Compilers; • Security and privacy → Cryptanalysis and other attacks. SpeculativeAbstract Interpretation Architectural Parameters (cache config., speculation depth) Control Flow Analysis

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Ascertaining Uncertainty for Efficient Exact Cache Analysis

Cited by 19 publications

References 18 publications

A fast analytical model of fully associative caches

A fast analytical model of fully associative caches

Fast and exact analysis for LRU caches

Abstract interpretation under speculative execution

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